A High-Resistance SOT Device Based Computing-in-Memory Macro With High Sensing Margin and Multi-Bit MAC Operations for AI Edge Inference

Computing-in-memory (CIM) offers a promising solution to the memory wall issue. Magnetoresistive random-access memory (MRAM) is a favored medium for CIM due to its non-volatility, high speed, low power, and technology maturity. However, MRAM has continuously encountered the challenge of an insufficient high-resistance state (HRS) to low-resistance state (LRS) ratio, which affects the result accuracy of CIM. In this paper, based on SOT devices, we propose a 5T2M bit-cell structure that increases the high-to-low current ratio by modulating the sub-threshold operation region. Besides, by jointly using high-resistance devices (M$\Omega$ level), the power consumption of the bit-cell array can be significantly reduced. Simultaneously, we have designed a compatible multi-bit implementation and macro architecture to support AI edge inference acceleration. This work was simulated under a 40-nm foundry process and a physically verified SOT-MTJ model. The results show that under the same high-to-low resistance ratio, a 52.6× high-to-low current ratio can be achieved, along with a 38.6%-98% bit-cell array power reduction.